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The current number of coronavirus (COVID-19) infections in Indonesia becomes more and more worrying. According to data on June 11, 2020, the number of infected people in Indonesia has reached 35,295 people. With these consequences, it is considered very important to immediately identify infection in order to stop or minimize the spread of the disease. There have been several ways to detect and diagnose COVID-19, one of which is using X-ray images. This paper examines the use of in-depth features and methods to process two-dimensional data from patients' X-ray images. Convolutional Neural Network (CNN) is a development of Multi-Layer Perceptron (MLP), which is specifically designed to process two-dimensional data or image data. The deep features of the fully connected layer CNN model are extracted and can be immediately classified without the need for any additional techniques. CNN method is used because of its good performance for large datasets that will be used for training and testing. In the classification process, the dataset contains 160 x-ray images and consists of two categories, COVID-19 and normal, that represents a positive or negative classification of Covid-19 infection to a patient. To get the best accuracy of the classification model, the author changed several parameters on CNN, such as the distribution of the dataset and the number of epochs. From the nine models tested, model number 5 and 8 with a dataset ratio of 70:30 and epoch number 30 and 40 respectively, resulted in the best accuracy of 97.91%.

Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

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Komponen penting yang dibutuhkan dalam sistem informasi atau perangkat lunak adalah basis data. Basis data membantu perangkat lunak dalam mengolah data yang datang dari input yang masuk ke dalam sistem. Untuk menjaga integritas dan keamanan data, programmer wajib memberikan fitur validasi data pada input. Validasi data dapat dilakukan dengan membuat batasan di tingkat aplikasi maupun di tingkat basis data. Sangat penting melakukan validasi data tingkat basis data tidak hanya pada tingkat pemrograman saja. LaundryPOS adalah aplikasi karis berbasis mobile yang diperuntukkan untuk usaha laundry. Penelitian ini akan melakukan analisis keuntungan dari CHECK constraint di database pada aplikasi LaundryPOS dalam aspek kebenaran data. Pengujian dilakukan dengan menggunakan query dan kendala. Hasil dari pengujian ini membuktikan bahwa constraint CHECK mampu menjaga aspek kebenaran pada basis data aplikasi LaundryPOS dengan menyaring data input yang tidak sesuai dengan format yang ditentukan.Kata Kunci—CHECK constraint, integritas data, validasi data, aspek kebenaran data, MySQLAn importantcomponents in the information system or software is database. The database helps the software process data that comes from the input that enters the system. To maintain data integrity and security, programmers must provide data validation features on the input. Data validation can be done by creating constraints at the application level or at the database level. It is very important to do database level data validation not only at the programming level. LaundryPOS is a mobile-based cashier application intended for laundry businesses. This study will analyze the benefits of CHECK constraints in the database on the LaundryPOS in terms of data correctness. Tests carried out using the query and constraints. The results of this test demonstrate that CHECK constraint is able to maintain the Correctness Aspects of the LaundryPOS database by filtering input data that does not match the specified format.Keywords—CHECK constraints, data integrity, data validation, aspek kebenaran data, MySQL